1. On Using Semantic Complex Event Processing for Dynamic Demand Response
Qunzhi Zhou, Yogesh Simmhan and Viktor Prasanna
Department of Computer Science, FZ
University of Southern California, Los Angeles
Submitted to SmartGridComm 2012

2. Semantic Information Modeling for Emerging Applications in Smart Grid
Demand Response
Objectives
Manage demand-side power load in response to supply conditions
Reduce the required maximum power generation capacities
Avoid starting and stopping generation units frequently
The state of the art
Schedule-based
Incentive-based
(1)
schedule-based, where curtailment is scheduled a priori based
on customer commitments, and (2) incentive-based, where
customers are offered pricing incentives during peak load
periods to encourage voluntary curtailment [2].
When there is limited co mmunication and monitoring info. This is what we can do
18-Sep-18
Semantic Information Modeling for Emerging Applications in Smart Grid

3. Semantic Information Modeling for Emerging Applications in Smart Grid
Smart Power Grid
Fine-grained monitoring of power grid activities
Spatial scale – spaces, devices, users and organizations
Temporal scale – interval based measurements
Indirect influencers – weather forecasts, traffic conditions, event schedules
Bi-directional communications
Management sites to end-use customers/devices
End-use customers/devices to management sites
Power grid is under rapid transition to smart grid.
Featured by
Upgrading generation/transmission/distribution
Integrating digital and information technologies
Leading to more efficient and reliable management of power systems
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Semantic Information Modeling for Emerging Applications in Smart Grid

4. Smart Grid Architecture
Advanced IT applications
Bi-directional communication
networks
Information sources
sensors, meters, smart appliances,
online services …
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Semantic Information Modeling for Emerging Applications in Smart Grid

5. Dynamic DR in Smart Grid
A New DR Paradigm
Leverage fine-grained Smart Grid monitoring information
Coordinate relevant information from multiple domains
Perform online information analysis for decision making
Solution Framework - Semantic Complex Event Processing
Abstract information as events associated with domain entities
Model decision making situations as event patterns
Process event streams to detect occurrences of patterns in real-time
Model domain-specific situations as information/event patterns
Perform online analysis of information streams for timely situation detection
Real-time monitoring information – event data tuples
Domain entities and knowledge – semantic meta data
DR situations – combinations/patterns of semantic events
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Semantic Information Modeling for Emerging Applications in Smart Grid

6. Semantic Information Modeling for Emerging Applications in Smart Grid
Contributions
DR Pattern Taxonomy
Categorize DR event patterns from different aspects
Offer example patterns in a campus micro grid scenario
Experimental Evaluation
Demonstrate the use of the SCEP framework for DR
Perform online analysis of campus micro grid data streams to detect DR situations
Exploit capabilitie sof scep for DR applications
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Semantic Information Modeling for Emerging Applications in Smart Grid

7. Semantic Information Modeling for Emerging Applications in Smart Grid
Campus Micro Grid
Grid Infrastructures
>170 buildings
Thousands of class rooms, meeting rooms lecture halls …
Numerous meters, sensors to measure from campus to device level consumption, temperature, airflow and occupancy changes …
DR Participants
Over 6,000 students, faculty and staff
Grouped in various organizations – schools, departments, facility management services
Evolving, multi-user role
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Semantic Information Modeling for Emerging Applications in Smart Grid

8. Semantic Complex Event Processing
Semantic Events
Real-time data streams linked with domain ontologies
DR Event Patterns
High level queries defined over
ontologies and streams
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Semantic Information Modeling for Emerging Applications in Smart Grid

9. Semantic Information Modeling for Emerging Applications in Smart Grid
DR Pattern Taxonomy
Top Level Categories
Classify DR patterns from different perspectives
Exploit the capabilities of SCEP in modeling DR applications
To fully exploit the capabilities of semantic complex event
processing for DR applications, we classify DR patterns from
different perspectives. Figure 2 shows top level categories of
this taxonomy, discussed further below. The examples we provide
are based on semantic concepts for and experiences in the
USC microgrid, but can be generalized to other environments.
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Semantic Information Modeling for Emerging Applications in Smart Grid

10. Semantic Information Modeling for Emerging Applications in Smart Grid
End-use Purpose
Monitoring an Prediction Patterns – when to perform DR (incrementally)
Curtailment Patterns – how to perform DR
To fully exploit the capabilities of semantic complex event
processing for DR applications, we classify DR patterns from
different perspectives. Figure 2 shows top level categories of
this taxonomy, discussed further below. The examples we provide
are based on semantic concepts for and experiences in the
USC microgrid, but can be generalized to other environments.
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Semantic Information Modeling for Emerging Applications in Smart Grid

11. Semantic Information Modeling for Emerging Applications in Smart Grid
Monitoring Pattern
Demand Monitoring
Response Monitoring
To fully exploit the capabilities of semantic complex event
processing for DR applications, we classify DR patterns from
different perspectives. Figure 2 shows top level categories of
this taxonomy, discussed further below. The examples we provide
are based on semantic concepts for and experiences in the
USC microgrid, but can be generalized to other environments.
18-Sep-18
Semantic Information Modeling for Emerging Applications in Smart Grid

12. Semantic Information Modeling for Emerging Applications in Smart Grid
Prediction Pattern
Space specific short-term predictions
Traditional demand forecast models
are not well suited for demand prediction at fine temporal
and spatial scale, particularly when consumption profiles
change [5]. However can be captured by intuitive patterns
To fully exploit the capabilities of semantic complex event
processing for DR applications, we classify DR patterns from
different perspectives. Figure 2 shows top level categories of
this taxonomy, discussed further below. The examples we provide
are based on semantic concepts for and experiences in the
USC microgrid, but can be generalized to other environments.
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Semantic Information Modeling for Emerging Applications in Smart Grid

13. Semantic Information Modeling for Emerging Applications in Smart Grid
Curtailment Pattern
Opportunistic versus Scheduled and Voluntary
Shave Pattern
Eliminate non-necessary or wasteful demands
Shift Pattern
Move non-urgent demand
Shape Pattern
Flatten demand
To fully exploit the capabilities of semantic complex event
processing for DR applications, we classify DR patterns from
different perspectives. Figure 2 shows top level categories of
this taxonomy, discussed further below. The examples we provide
are based on semantic concepts for and experiences in the
USC microgrid, but can be generalized to other environments.
18-Sep-18
Semantic Information Modeling for Emerging Applications in Smart Grid

14. Spatial and Temporal Scale
Define patterns based on objective spaces
Detect patterns at different time scales of data streams
To fully exploit the capabilities of semantic complex event
processing for DR applications, we classify DR patterns from
different perspectives. Figure 2 shows top level categories of
this taxonomy, discussed further below. The examples we provide
are based on semantic concepts for and experiences in the
USC microgrid, but can be generalized to other environments.
18-Sep-18
Semantic Information Modeling for Emerging Applications in Smart Grid

15. Semantic Information Modeling for Emerging Applications in Smart Grid
Spatial Scale
Physic Space and Equipment
Virtual Space
customer groups, departments and so on
To fully exploit the capabilities of semantic complex event
processing for DR applications, we classify DR patterns from
different perspectives. Figure 2 shows top level categories of
this taxonomy, discussed further below. The examples we provide
are based on semantic concepts for and experiences in the
USC microgrid, but can be generalized to other environments.
18-Sep-18
Semantic Information Modeling for Emerging Applications in Smart Grid

16. Representation, Life Cycle and Adaptivity
Pattern abstraction level
Life Cycle
Time periods during which a pattern is active
Adaptivity
Pattern evolving over time?
To fully exploit the capabilities of semantic complex event
processing for DR applications, we classify DR patterns from
different perspectives. Figure 2 shows top level categories of
this taxonomy, discussed further below. The examples we provide
are based on semantic concepts for and experiences in the
USC microgrid, but can be generalized to other environments.
18-Sep-18
Semantic Information Modeling for Emerging Applications in Smart Grid

17. Case Study in Campus Micro Grid (1)
Event Streams
Event Patterns
Example pattern 1-6
To fully exploit the capabilities of semantic complex event
processing for DR applications, we classify DR patterns from
different perspectives. Figure 2 shows top level categories of
this taxonomy, discussed further below. The examples we provide
are based on semantic concepts for and experiences in the
USC microgrid, but can be generalized to other environments.
18-Sep-18
Semantic Information Modeling for Emerging Applications in Smart Grid

18. Case Study in Campus Micro Grid (2)
Empirical Evaluations
To fully exploit the capabilities of semantic complex event
processing for DR applications, we classify DR patterns from
different perspectives. Figure 2 shows top level categories of
this taxonomy, discussed further below. The examples we provide
are based on semantic concepts for and experiences in the
USC microgrid, but can be generalized to other environments.
18-Sep-18
Semantic Information Modeling for Emerging Applications in Smart Grid

19. Semantic Information Modeling for Emerging Applications in Smart Grid
Discussion
The state-of-the-art DR approaches are based on static planning
Smart Grid provides opportunities for more fine-grained DR in both spatial and temporal scale
Semantic complex event processing is a promising information processing paradigm for dynamic DR
The CEP based data-driven opportunistic DR is a vital supplement to traditional schedule an incentive driven approaches
The next step is to extend the case studies on campus
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Semantic Information Modeling for Emerging Applications in Smart Grid

20. Semantic Information Modeling for Emerging Applications in Smart Grid
Thank You!
18-Sep-18
Semantic Information Modeling for Emerging Applications in Smart Grid